Hardware-in-the-Loop Assessment of a Data-Driven State of Charge Estimation Method for Lithium-Ion Batteries in Hybrid Vehicles

This paper presents the design and hardware-in-the-loop (HIL) experimental validation of a data-driven estimation method for the state of charge (<i>SOC</i>) in the lithium-ion batteries used in hybrid electric vehicles (HEVs). The considered system features a 1.25 kWh 48 V lithium-ion b...

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Autores principales:	Sara Luciani, Stefano Feraco, Angelo Bonfitto, Andrea Tonoli
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	battery monitoring system state of charge artificial neural networks hardware-in-the-loop real-time hardware modeling Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/d4debc727c9646eeaa79fbff5aa501f1
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id	oai:doaj.org-article:d4debc727c9646eeaa79fbff5aa501f1
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spelling	oai:doaj.org-article:d4debc727c9646eeaa79fbff5aa501f12021-11-25T17:25:02ZHardware-in-the-Loop Assessment of a Data-Driven State of Charge Estimation Method for Lithium-Ion Batteries in Hybrid Vehicles10.3390/electronics102228282079-9292https://doaj.org/article/d4debc727c9646eeaa79fbff5aa501f12021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2828https://doaj.org/toc/2079-9292This paper presents the design and hardware-in-the-loop (HIL) experimental validation of a data-driven estimation method for the state of charge (<i>SOC</i>) in the lithium-ion batteries used in hybrid electric vehicles (HEVs). The considered system features a 1.25 kWh 48 V lithium-ion battery that is numerically modeled via an RC equivalent circuit model that can also consider the environmental temperature influence. The proposed estimation technique relies on nonlinear autoregressive with exogenous input (NARX) artificial neural networks (ANNs) that are properly trained with multiple datasets. Those datasets include modeled current and voltage data, both for charge-sustaining and charge-depleting working conditions. The investigated method is then experimentally validated using a Raspberry Pi 4B card-sized board, on which the estimation algorithm is actually deployed, and real-time hardware, on which the battery model is developed, namely a Speedgoat baseline platform. These hardware platforms are used in a hardware-in-the-loop architecture via the UPD communication protocol, allowing the system to be validated in a proper testing environment. The resulting estimation algorithm can estimate the battery <i>SOC</i> in real-time, with 2% accuracy during real-time hardware testing.Sara LucianiStefano FeracoAngelo BonfittoAndrea TonoliMDPI AGarticlebattery monitoring systemstate of chargeartificial neural networkshardware-in-the-loopreal-time hardwaremodelingElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2828, p 2828 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	battery monitoring system state of charge artificial neural networks hardware-in-the-loop real-time hardware modeling Electronics TK7800-8360
spellingShingle	battery monitoring system state of charge artificial neural networks hardware-in-the-loop real-time hardware modeling Electronics TK7800-8360 Sara Luciani Stefano Feraco Angelo Bonfitto Andrea Tonoli Hardware-in-the-Loop Assessment of a Data-Driven State of Charge Estimation Method for Lithium-Ion Batteries in Hybrid Vehicles
description	This paper presents the design and hardware-in-the-loop (HIL) experimental validation of a data-driven estimation method for the state of charge (<i>SOC</i>) in the lithium-ion batteries used in hybrid electric vehicles (HEVs). The considered system features a 1.25 kWh 48 V lithium-ion battery that is numerically modeled via an RC equivalent circuit model that can also consider the environmental temperature influence. The proposed estimation technique relies on nonlinear autoregressive with exogenous input (NARX) artificial neural networks (ANNs) that are properly trained with multiple datasets. Those datasets include modeled current and voltage data, both for charge-sustaining and charge-depleting working conditions. The investigated method is then experimentally validated using a Raspberry Pi 4B card-sized board, on which the estimation algorithm is actually deployed, and real-time hardware, on which the battery model is developed, namely a Speedgoat baseline platform. These hardware platforms are used in a hardware-in-the-loop architecture via the UPD communication protocol, allowing the system to be validated in a proper testing environment. The resulting estimation algorithm can estimate the battery <i>SOC</i> in real-time, with 2% accuracy during real-time hardware testing.
format	article
author	Sara Luciani Stefano Feraco Angelo Bonfitto Andrea Tonoli
author_facet	Sara Luciani Stefano Feraco Angelo Bonfitto Andrea Tonoli
author_sort	Sara Luciani
title	Hardware-in-the-Loop Assessment of a Data-Driven State of Charge Estimation Method for Lithium-Ion Batteries in Hybrid Vehicles
title_short	Hardware-in-the-Loop Assessment of a Data-Driven State of Charge Estimation Method for Lithium-Ion Batteries in Hybrid Vehicles
title_full	Hardware-in-the-Loop Assessment of a Data-Driven State of Charge Estimation Method for Lithium-Ion Batteries in Hybrid Vehicles
title_fullStr	Hardware-in-the-Loop Assessment of a Data-Driven State of Charge Estimation Method for Lithium-Ion Batteries in Hybrid Vehicles
title_full_unstemmed	Hardware-in-the-Loop Assessment of a Data-Driven State of Charge Estimation Method for Lithium-Ion Batteries in Hybrid Vehicles
title_sort	hardware-in-the-loop assessment of a data-driven state of charge estimation method for lithium-ion batteries in hybrid vehicles
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d4debc727c9646eeaa79fbff5aa501f1
work_keys_str_mv	AT saraluciani hardwareintheloopassessmentofadatadrivenstateofchargeestimationmethodforlithiumionbatteriesinhybridvehicles AT stefanoferaco hardwareintheloopassessmentofadatadrivenstateofchargeestimationmethodforlithiumionbatteriesinhybridvehicles AT angelobonfitto hardwareintheloopassessmentofadatadrivenstateofchargeestimationmethodforlithiumionbatteriesinhybridvehicles AT andreatonoli hardwareintheloopassessmentofadatadrivenstateofchargeestimationmethodforlithiumionbatteriesinhybridvehicles
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Hardware-in-the-Loop Assessment of a Data-Driven State of Charge Estimation Method for Lithium-Ion Batteries in Hybrid Vehicles

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